In:
Journal of Engineering for Gas Turbines and Power, ASME International, Vol. 142, No. 1 ( 2020-01-01)
Abstract:
The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Microgas turbines (mGTs) constitute a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of postcombustion carbon capture (CC) on these energy systems. Despite this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with exhaust gas recirculation has been coupled with an amine-based CC plant and simulated using the software aspenplus. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian process regression (GPR) model, trained using the aspenplus data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a stochastic optimization has been carried out. As a general result, the analyzed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.
Type of Medium:
Online Resource
ISSN:
0742-4795
,
1528-8919
Language:
English
Publisher:
ASME International
Publication Date:
2020
detail.hit.zdb_id:
2010437-6
detail.hit.zdb_id:
165371-4
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